Correlation Between ITT and NET Power
Can any of the company-specific risk be diversified away by investing in both ITT and NET Power at the same time? Although using a correlation coefficient on its own may not help to predict future stock returns, this module helps to understand the diversifiable risk of combining ITT and NET Power into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between ITT Inc and NET Power, you can compare the effects of market volatilities on ITT and NET Power and check how they will diversify away market risk if combined in the same portfolio for a given time horizon. You can also utilize pair trading strategies of matching a long position in ITT with a short position of NET Power. Check out your portfolio center. Please also check ongoing floating volatility patterns of ITT and NET Power.
Diversification Opportunities for ITT and NET Power
Very weak diversification
The 3 months correlation between ITT and NET is 0.4. Overlapping area represents the amount of risk that can be diversified away by holding ITT Inc and NET Power in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on NET Power and ITT is a relative statistical measure of the degree to which these equity instruments tend to move together. The correlation coefficient measures the extent to which returns on ITT Inc are associated (or correlated) with NET Power. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of NET Power has no effect on the direction of ITT i.e., ITT and NET Power go up and down completely randomly.
Pair Corralation between ITT and NET Power
Considering the 90-day investment horizon ITT Inc is expected to under-perform the NET Power. But the stock apears to be less risky and, when comparing its historical volatility, ITT Inc is 3.48 times less risky than NET Power. The stock trades about -0.25 of its potential returns per unit of risk. The NET Power is currently generating about -0.01 of returns per unit of risk over similar time horizon. If you would invest 1,070 in NET Power on October 10, 2024 and sell it today you would lose (30.00) from holding NET Power or give up 2.8% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
ITT Inc vs. NET Power
Performance |
Timeline |
ITT Inc |
NET Power |
ITT and NET Power Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with ITT and NET Power
The main advantage of trading using opposite ITT and NET Power positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if ITT position performs unexpectedly, NET Power can make up some of the losses. Pair trading also minimizes risk from directional movements in the market. For example, if an entire industry or sector drops because of unexpected headlines, the short position in NET Power will offset losses from the drop in NET Power's long position.The idea behind ITT Inc and NET Power pairs trading is to make the combined position market-neutral, meaning the overall market's direction will not affect its win or loss (or potential downside or upside). This can be achieved by designing a pairs trade with two highly correlated stocks or equities that operate in a similar space or sector, making it possible to obtain profits through simple and relatively low-risk investment.NET Power vs. Porvair plc | NET Power vs. HNI Corp | NET Power vs. LAir Liquide SA | NET Power vs. Perseus Mining Limited |
Check out your portfolio center.Note that this page's information should be used as a complementary analysis to find the right mix of equity instruments to add to your existing portfolios or create a brand new portfolio. You can also try the Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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